The prostheses used by the majority of persons with upper limb amputations today offer a limited range of motion. Relative to anatomical joints transradial (below the elbow) prosthesis users lose at least two of the three degrees of freedom provided by the wrist and forearm. Some myoeletric prostheses currently allow for forearm pronation and supination (rotation about an axis parallel to the forearm) and the operation of a powered prosthetic hand. Body-powered prostheses, incorporating hooks and other cable driven terminal devices, have even fewer active degrees of freedom. In order to perform activities of daily living, an amputee must use a greater than normal range of movement from other anatomical body joints to compensate for the loss of movement caused by the amputation. By studying this compensatory motion of prosthetic users, the mechanics of how they adapt to the loss of range of motion in a given limb and specific tasks were analyzed. The purpose of this study is to create a robotic based kinematic model that can predict the compensatory motion of a given task using given subject data in select tasks. The tasks used in this study are the activities of daily living: opening a door, drinking from a cup, lifting a box, and turning a steering wheel. For the model the joint angles necessary to accomplish a task are calculated by a simulation for a set of prostheses and tasks. The simulation contains a set of configurations that are represented by parameters that consist of the joint degrees of freedom provided by each prosthesis, and a set of task information that includes joint constraints and trajectories. In the simulation the hand or prosthesis follows the trajectory to perform the task. Analysis of tasks is done by attaching prosthetic constraints to one of the arms of the upper body model in the simulation, other arm maintains an anatomical configuration. By running the model through this simulation with different configurations the compensatory motions were found. Results can then be used to select the best prosthesis for a given user, design prostheses that are more effective at selected tasks, and demonstrate some possible compensations given a set of residual joint limitations with certain prosthetic components, by optimizing the configuration of the prostheses to improve their performance.

This work focuses on the use of 3D motion capture data to create and optimize a robotic human body model (RHBM) to predict the inverse kinematics of the upper body. The RHBM is a 25 degrees of freedom (DoFs) upper body model with subject specific kinematic parameters. The model was developed to predict the inverse kinematics of the upper body in the simulation of a virtual person, including persons with functional limitations such as a transradial or transhumeral amputation. Motion data were collected from 14 subjects: 10 non-amputees control subjects, 1 person with a transradial amputation, and 3 persons with a transhumeral amputation, in the University of South Florida's (USF) motion analysis laboratory. Motion capture for each subject consisted of the repetition of a series of range of motion (RoM) tasks and activities of daily living (ADLs), which were recorded using an eight camera Vicon (Oxford, UK) motion analysis system. The control subjects were also asked to repeat the motions while wearing a brace on their dominant arm. The RoM tasks consisted of elbow flexion & extension, forearm pronation & supination, shoulder flexion & extension, shoulder abduction & adduction, shoulder rotation, torso flexion & extension, torso lateral flexion, and torso rotation. The ADLs evaluated were brushing one's hair, drinking from a cup, eating with a knife and fork, lifting a laundry basket, and opening a door. The impact of bracing and prosthetic devices on the subjects' RoM, and their motion during ADLs was analyzed. The segment geometries of the subjects' upper body were extracted directly from the motion analysis data using a functional joint center method. With this method there are no conventional or segment length differences between recorded data segments and the RHBM. This ensures the accuracy of the RHBM when reconstructing a recorded task, as the model has the same geometry as the recorded data. A detailed investigation of the weighted least norm, probability density gradient projection method, artificial neural networks was performed to optimize the redundancy RHBM inverse kinematics. The selected control algorithm consisted of a combination of the weighted least norm method and the gradient projection of the null space, minimizing the inverse of the probability density function. This method increases the accuracy of the RHBM while being suitable for a wide range of tasks and observing the required subject constraint inputs.